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Estimating large-scale factor models for economic activity in Germany : do they outperform simpler models?

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  • Dreger, Christian
  • Schumacher, Christian

Abstract

This paper discusses a large-scale factor model for the German economy. Following the recent literature, a data set of 121 time series is used via principal component analysis to determine the factors, which enter a dynamic model for German GDP. The model is compared with alternative univariate and multivariate models. These models are based on regression techniques and considerably smaller data sets. Out-of-sample forecasts show that the prediction errors of the factor model are smaller than the errors of the rival models. However, these advantages are not statistically significant, as a test for equal forecast accuracy shows. Therefore, the effciency gains of using a large data set with this kind of factor models seem to be limited.

Suggested Citation

  • Dreger, Christian & Schumacher, Christian, 2002. "Estimating large-scale factor models for economic activity in Germany : do they outperform simpler models?," HWWA Discussion Papers 199, Hamburg Institute of International Economics (HWWA).
  • Handle: RePEc:zbw:hwwadp:26321
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    References listed on IDEAS

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    Cited by:

    1. Klaus Abberger & Sascha Becker & Barbara Hofmann & Klaus Wohlrabe, 2007. "Mikrodaten im ifo Institut für Wirtschaftsforschung – Bestand, Verwendung und Zugang," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 1(1), pages 27-42, June.
    2. Bušs, Ginters, 2009. "Comparing forecasts of Latvia's GDP using simple seasonal ARIMA models and direct versus indirect approach," MPRA Paper 16684, University Library of Munich, Germany.
    3. Charles Rahal, 2015. "House Price Forecasts with Factor Combinations," Discussion Papers 15-05, Department of Economics, University of Birmingham.
    4. Viktors Ajevskis & Gundars Davidsons, 2008. "Dynamic Factor Models in Forecasting Latvia's Gross Domestic Product," Working Papers 2008/02, Latvijas Banka.
    5. Christophe Van Nieuwenhuyze, 2006. "A generalised dynamic factor model for the Belgian economy - Useful business cycle indicators and GDP growth forecasts," Working Paper Research 80, National Bank of Belgium.
    6. Kristensen Johannes Tang, 2014. "Factor-based forecasting in the presence of outliers: Are factors better selected and estimated by the median than by the mean?," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(3), pages 1-30, May.
    7. Jason Angelopoulos & Costas I. Chlomoudis, 2017. "A Generalized Dynamic Factor Model for the U.S. Port Sector," SPOUDAI Journal of Economics and Business, SPOUDAI Journal of Economics and Business, University of Piraeus, vol. 67(1), pages 22-37, January-M.
    8. Ard H.J. den Reijer, 2005. "Forecasting Dutch GDP using Large Scale Factor Models," DNB Working Papers 028, Netherlands Central Bank, Research Department.
    9. Sascha O. Becker & Klaus Wohlrabe, 2008. "European Data Watch: Micro Data at the Ifo Institute for Economic Research – The “Ifo Business Survey”, Usage and Access," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 128(2), pages 307-319.
    10. Anna Sophia Ciesielski & Klaus Wohlrabe, 2011. "Sektorale Prognosen im Verarbeitenden Gewerbe," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 64(22), pages 27-35, November.
    11. Bessonovs, Andrejs, 2010. "Faktoru modeļu agregēta un dezagregēta pieeja IKP prognožu precizitātes mērīšanā
      [Measuring GDP forecasting accuracy using factor models: aggregated vs. disaggregated approach]
      ," MPRA Paper 30386, University Library of Munich, Germany.
    12. Klaus Abberger & Klaus Wohlrabe, 2006. "Einige Prognoseeigenschaften des ifo Geschäftsklimas - Ein Überblick über die neuere wissenschaftliche Literatur," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 59(22), pages 19-26, November.
    13. repec:wly:japmet:v:33:y:2018:i:3:p:355-377 is not listed on IDEAS
    14. In Choi, 2011. "Efficient Estimation of Nonstationary Factor Models," Working Papers 1101, Research Institute for Market Economy, Sogang University, revised Jun 2011.
    15. Klaus Abberger & Gebhard Flaig & Wolfgang Nierhaus, 2007. "ifo Konjunkturumfragen und Konjunkturanalyse : ausgewählte methodische Aufsätze aus dem ifo Schnelldienst," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 33, October.
    16. In Choi & Dukpa Kim & Yun Jung Kim & Noh†Sun Kwark, 2018. "A multilevel factor model: Identification, asymptotic theory and applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(3), pages 355-377, April.
    17. Charles Rahal, 2015. "Housing Market Forecasting with Factor Combinations," Discussion Papers 15-05r, Department of Economics, University of Birmingham.
    18. Kitlinski, Tobias, 2015. "With or without you: Do financial data help to forecast industrial production?," Ruhr Economic Papers 558, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    19. Lorenzo Boldrini & Eric Hillebrand, 2015. "Supervision in Factor Models Using a Large Number of Predictors," CREATES Research Papers 2015-38, Department of Economics and Business Economics, Aarhus University.
    20. Kitlinski, Tobias & an de Meulen, Philipp, 2015. "The role of targeted predictors for nowcasting GDP with bridge models: Application to the Euro area," Ruhr Economic Papers 559, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.

    More about this item

    Keywords

    Factor models; Principal components; forecasting accuracy;

    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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